For this particular assignment, the data of different types of wine sales in the 20th century is to be analysed. Both of these data are from the same company but of different wines. As an analyst in the ABC Estate Wines, you are tasked to analyse and forecast Wine Sales in the 20th century.
from statsmodels.tsa.arima.model import ARIMA as ar
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.linear_model import LinearRegression
import statsmodels as st
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
from statsmodels.tsa.arima.model import ARIMA
import itertools
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import numpy as np
import pandas as pd
from sklearn import metrics
import matplotlib.pyplot as plt
#import plotly.offline as py
%matplotlib inline
import seaborn as sns
from pylab import rcParams
df = pd.read_csv("D:/Deakin university master degree/time series forcasting/project/Sparkling.csv") ## Fill the blank to read the data
df.head() ## Complete the code to view top 5 rows of the data
| YearMonth | Sparkling | |
|---|---|---|
| 0 | 1980-01 | 1686 |
| 1 | 1980-02 | 1591 |
| 2 | 1980-03 | 2304 |
| 3 | 1980-04 | 1712 |
| 4 | 1980-05 | 1471 |
# checking shape of the data
print(f"There are {df.shape[0]} rows and {df.shape[1]} columns.") # Complete the code to view dimensions of the data
There are 187 rows and 2 columns.
# checking column datatypes and number of non-null values
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 187 entries, 0 to 186 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 YearMonth 187 non-null object 1 Sparkling 187 non-null int64 dtypes: int64(1), object(1) memory usage: 3.1+ KB
Observations
YearMonth is object type columns and Sparkling is numerical type column.Time_Stamp = pd.date_range(start='1980-01-01',periods=len(df),freq='M')
Time_Stamp
DatetimeIndex(['1980-01-31', '1980-02-29', '1980-03-31', '1980-04-30',
'1980-05-31', '1980-06-30', '1980-07-31', '1980-08-31',
'1980-09-30', '1980-10-31',
...
'1994-10-31', '1994-11-30', '1994-12-31', '1995-01-31',
'1995-02-28', '1995-03-31', '1995-04-30', '1995-05-31',
'1995-06-30', '1995-07-31'],
dtype='datetime64[ns]', length=187, freq='M')
df['Time_Stamp'] = Time_Stamp
df.head()
| YearMonth | Sparkling | Time_Stamp | |
|---|---|---|---|
| 0 | 1980-01 | 1686 | 1980-01-31 |
| 1 | 1980-02 | 1591 | 1980-02-29 |
| 2 | 1980-03 | 2304 | 1980-03-31 |
| 3 | 1980-04 | 1712 | 1980-04-30 |
| 4 | 1980-05 | 1471 | 1980-05-31 |
df.set_index(keys='Time_Stamp',inplace=True)
df
| YearMonth | Sparkling | |
|---|---|---|
| Time_Stamp | ||
| 1980-01-31 | 1980-01 | 1686 |
| 1980-02-29 | 1980-02 | 1591 |
| 1980-03-31 | 1980-03 | 2304 |
| 1980-04-30 | 1980-04 | 1712 |
| 1980-05-31 | 1980-05 | 1471 |
| ... | ... | ... |
| 1995-03-31 | 1995-03 | 1897 |
| 1995-04-30 | 1995-04 | 1862 |
| 1995-05-31 | 1995-05 | 1670 |
| 1995-06-30 | 1995-06 | 1688 |
| 1995-07-31 | 1995-07 | 2031 |
187 rows × 2 columns
df.drop(['YearMonth'],axis=1,inplace=True) #Complete the code to drop the column 'YearMonth'
print(df.shape)
(187, 1)
Before we start exploring the data further, let's quickly check the missingness in the data.
df.isnull().sum() # Complete the code to check the presence of missing values
Sparkling 0 dtype: int64
df.head() ## Complete the code to view top 5 rows of the data
| Sparkling | |
|---|---|
| Time_Stamp | |
| 1980-01-31 | 1686 |
| 1980-02-29 | 1591 |
| 1980-03-31 | 2304 |
| 1980-04-30 | 1712 |
| 1980-05-31 | 1471 |
Timestamp vs Sparkling¶## let's plot the sparkling vs timestamp
plt.figure(figsize=(22, 8))
plt.plot(df);
plt.grid()
plt.figure(figsize=(22, 8))
sns.boxplot(x = df.index.year,y = df['Sparkling']) # Complete the code to check the relationship between the 'Sparkling' column and 'Time_Stamp'
plt.grid();
plt.figure(figsize=(22, 8))
sns.boxplot(x = df.index.month_name(),y = df['Sparkling']) # Complete the code to check the relationship between the 'Sparkling' column and 'Time_Stamp'
plt.grid();
October, November , December are high seanons
decomposition = seasonal_decompose(df,model='additive') ##Complete the code to check additive Decomposition
decomposition.plot();
trend = decomposition.trend
seasonality = decomposition.seasonal
residual = decomposition.resid
print('Trend','\n',trend.head(12),'\n')
print('Seasonality','\n',seasonality.head(12),'\n')
print('Residual','\n',residual.head(12),'\n')
Trend Time_Stamp 1980-01-31 NaN 1980-02-29 NaN 1980-03-31 NaN 1980-04-30 NaN 1980-05-31 NaN 1980-06-30 NaN 1980-07-31 2360.666667 1980-08-31 2351.333333 1980-09-30 2320.541667 1980-10-31 2303.583333 1980-11-30 2302.041667 1980-12-31 2293.791667 Name: trend, dtype: float64 Seasonality Time_Stamp 1980-01-31 -854.260599 1980-02-29 -830.350678 1980-03-31 -592.356630 1980-04-30 -658.490559 1980-05-31 -824.416154 1980-06-30 -967.434011 1980-07-31 -465.502265 1980-08-31 -214.332821 1980-09-30 -254.677265 1980-10-31 599.769957 1980-11-30 1675.067179 1980-12-31 3386.983846 Name: seasonal, dtype: float64 Residual Time_Stamp 1980-01-31 NaN 1980-02-29 NaN 1980-03-31 NaN 1980-04-30 NaN 1980-05-31 NaN 1980-06-30 NaN 1980-07-31 70.835599 1980-08-31 315.999487 1980-09-30 -81.864401 1980-10-31 -307.353290 1980-11-30 109.891154 1980-12-31 -501.775513 Name: resid, dtype: float64
residual.mean()
-1.2088458994707376
##Normality Distribution of Resid
from scipy.stats import shapiro
sns.distplot(residual)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\2194742101.py:4: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(residual)
<Axes: xlabel='resid', ylabel='Density'>
shapiro(residual.dropna())
ShapiroResult(statistic=0.9833053350448608, pvalue=0.0343334935605526)
#Multiplicative Decomposition
decomposition = seasonal_decompose(df,model='multiplicative') # complete the code to multiplicative decomposition
decomposition.plot();
trend = decomposition.trend
seasonality = decomposition.seasonal
residual = decomposition.resid
print('Trend','\n',trend.head(12),'\n')
print('Seasonality','\n',seasonality.head(12),'\n')
print('Residual','\n',residual.head(12),'\n')
Trend Time_Stamp 1980-01-31 NaN 1980-02-29 NaN 1980-03-31 NaN 1980-04-30 NaN 1980-05-31 NaN 1980-06-30 NaN 1980-07-31 2360.666667 1980-08-31 2351.333333 1980-09-30 2320.541667 1980-10-31 2303.583333 1980-11-30 2302.041667 1980-12-31 2293.791667 Name: trend, dtype: float64 Seasonality Time_Stamp 1980-01-31 0.649843 1980-02-29 0.659214 1980-03-31 0.757440 1980-04-30 0.730351 1980-05-31 0.660609 1980-06-30 0.603468 1980-07-31 0.809164 1980-08-31 0.918822 1980-09-30 0.894367 1980-10-31 1.241789 1980-11-30 1.690158 1980-12-31 2.384776 Name: seasonal, dtype: float64 Residual Time_Stamp 1980-01-31 NaN 1980-02-29 NaN 1980-03-31 NaN 1980-04-30 NaN 1980-05-31 NaN 1980-06-30 NaN 1980-07-31 1.029230 1980-08-31 1.135407 1980-09-30 0.955954 1980-10-31 0.907513 1980-11-30 1.050423 1980-12-31 0.946770 Name: resid, dtype: float64
residual = decomposition.resid
residual.mean()
0.9997456359115032
sns.distplot(residual)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1108040850.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(residual)
<Axes: xlabel='resid', ylabel='Density'>
shapiro(residual.dropna())
ShapiroResult(statistic=0.9859988689422607, pvalue=0.07802142202854156)
df.describe()
| Sparkling | |
|---|---|
| count | 187.000000 |
| mean | 2402.417112 |
| std | 1295.111540 |
| min | 1070.000000 |
| 25% | 1605.000000 |
| 50% | 1874.000000 |
| 75% | 2549.000000 |
| max | 7242.000000 |
df.index.year.unique() ## Complete the code to check the unique values
Int64Index([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
1991, 1992, 1993, 1994, 1995],
dtype='int64', name='Time_Stamp')
### Complete the code to take all data till the year 1991 in the train set and everything after that in the test set
df_train = df[df.index.year <= 1991]
df_test = df[df.index.year > 1991]
print(df_train.shape)
print(df_test.shape)
(144, 1) (43, 1)
df_train.head()
| Sparkling | |
|---|---|
| Time_Stamp | |
| 1980-01-31 | 1686 |
| 1980-02-29 | 1591 |
| 1980-03-31 | 2304 |
| 1980-04-30 | 1712 |
| 1980-05-31 | 1471 |
df_test.head()
| Sparkling | |
|---|---|
| Time_Stamp | |
| 1992-01-31 | 1577 |
| 1992-02-29 | 1667 |
| 1992-03-31 | 1993 |
| 1992-04-30 | 1997 |
| 1992-05-31 | 1783 |
train_time = [i+1 for i in range(len(df_train))]
test_time = [i+133 for i in range(len(df_test))]
print('Training Time instance','\n',train_time)
print('Test Time instance','\n',test_time)
Training Time instance [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144] Test Time instance [133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]
LinearRegression_train = df_train.copy()
LinearRegression_test = df_test.copy()
LinearRegression_train['time'] = train_time
LinearRegression_test['time'] = test_time
print('First few rows of Training Data','\n',LinearRegression_train.head(),'\n')
print('Last few rows of Training Data','\n',LinearRegression_train.tail(),'\n')
print('First few rows of Test Data','\n',LinearRegression_test.head(),'\n')
print('Last few rows of Test Data','\n',LinearRegression_test.tail(),'\n')
First few rows of Training Data
Sparkling time
Time_Stamp
1980-01-31 1686 1
1980-02-29 1591 2
1980-03-31 2304 3
1980-04-30 1712 4
1980-05-31 1471 5
Last few rows of Training Data
Sparkling time
Time_Stamp
1991-08-31 1857 140
1991-09-30 2408 141
1991-10-31 3252 142
1991-11-30 3627 143
1991-12-31 6153 144
First few rows of Test Data
Sparkling time
Time_Stamp
1992-01-31 1577 133
1992-02-29 1667 134
1992-03-31 1993 135
1992-04-30 1997 136
1992-05-31 1783 137
Last few rows of Test Data
Sparkling time
Time_Stamp
1995-03-31 1897 171
1995-04-30 1862 172
1995-05-31 1670 173
1995-06-30 1688 174
1995-07-31 2031 175
lr = LinearRegression()
LinearRegression_train['Sparkling'].values
array([1686, 1591, 2304, 1712, 1471, 1377, 1966, 2453, 1984, 2596, 4087,
5179, 1530, 1523, 1633, 1976, 1170, 1480, 1781, 2472, 1981, 2273,
3857, 4551, 1510, 1329, 1518, 1790, 1537, 1449, 1954, 1897, 1706,
2514, 3593, 4524, 1609, 1638, 2030, 1375, 1320, 1245, 1600, 2298,
2191, 2511, 3440, 4923, 1609, 1435, 2061, 1789, 1567, 1404, 1597,
3159, 1759, 2504, 4273, 5274, 1771, 1682, 1846, 1589, 1896, 1379,
1645, 2512, 1771, 3727, 4388, 5434, 1606, 1523, 1577, 1605, 1765,
1403, 2584, 3318, 1562, 2349, 3987, 5891, 1389, 1442, 1548, 1935,
1518, 1250, 1847, 1930, 2638, 3114, 4405, 7242, 1853, 1779, 2108,
2336, 1728, 1661, 2230, 1645, 2421, 3740, 4988, 6757, 1757, 1394,
1982, 1650, 1654, 1406, 1971, 1968, 2608, 3845, 4514, 6694, 1720,
1321, 1859, 1628, 1615, 1457, 1899, 1605, 2424, 3116, 4286, 6047,
1902, 2049, 1874, 1279, 1432, 1540, 2214, 1857, 2408, 3252, 3627,
6153], dtype=int64)
lr.fit(LinearRegression_train[['time']],LinearRegression_train['Sparkling'].values)
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
test_prediction_model = lr.predict(LinearRegression_test[['time']])
LinearRegression_test['RegOnTime']=test_prediction_model
plt.figure(figsize=(15,12))
plt.plot(df_train['Sparkling'],label='Train')
plt.plot(df_test['Sparkling'],label='Test')
plt.plot(LinearRegression_test['RegOnTime'],label='Regression On Time_Test Data')
plt.legend(loc='best')
plt.grid();
rmse_model_test= metrics.mean_squared_error(df_test['Sparkling'],test_prediction_model,squared=False)
print("For RegressionOnTime forecast on the Test Data, RMSE is %3.3f" %(rmse_model_test))
For RegressionOnTime forecast on the Test Data, RMSE is 1337.090
resultsDf = pd.DataFrame({'Test RMSE': [rmse_model_test]},index=['RegressionOnTime'])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.09022 |
For this particular naive model, we say that the prediction for tomorrow is the same as today and the prediction for day after tomorrow is tomorrow and since the prediction of tomorrow is same as today,therefore the prediction for day after tomorrow is also today.
NaiveModel_train = df_train.copy() # Complete the code to create a copy of the train datasets
NaiveModel_test = df_test.copy() # Complete the code to create a copy of the test datasets
NaiveModel_train.shape
(144, 1)
NaiveModel_test.shape
(43, 1)
NaiveModel_test.head()
| Sparkling | |
|---|---|
| Time_Stamp | |
| 1992-01-31 | 1577 |
| 1992-02-29 | 1667 |
| 1992-03-31 | 1993 |
| 1992-04-30 | 1997 |
| 1992-05-31 | 1783 |
NaiveModel_test['naive'] = np.asarray(df_train['Sparkling'])[len(np.asarray(df_train['Sparkling']))-1]
NaiveModel_test['naive'].head()
Time_Stamp 1992-01-31 6153 1992-02-29 6153 1992-03-31 6153 1992-04-30 6153 1992-05-31 6153 Name: naive, dtype: int64
NaiveModel_test.head()
| Sparkling | naive | |
|---|---|---|
| Time_Stamp | ||
| 1992-01-31 | 1577 | 6153 |
| 1992-02-29 | 1667 | 6153 |
| 1992-03-31 | 1993 | 6153 |
| 1992-04-30 | 1997 | 6153 |
| 1992-05-31 | 1783 | 6153 |
plt.figure(figsize=(15,12))
plt.plot(NaiveModel_train['Sparkling'],label='Train')
plt.plot(df_test['Sparkling'],label='Test')
plt.plot(NaiveModel_test['naive'], label='Naive Forecast on test data')
plt.legend(loc='best')
plt.title('Naive Forecast')
plt.grid();
##MODEL EVAULATION
rmse_model_test = metrics.mean_squared_error(df_test['Sparkling'],NaiveModel_test['naive'],squared=False)
print("For Naive On Time Forecast on the Test Data, RMSE is %3.3f" %(rmse_model_test))
For Naive On Time Forecast on the Test Data, RMSE is 3979.915
resultsDfN = pd.DataFrame({'Test RMSE': [rmse_model_test]}, index=['NaiveModel']) # Complete the code to check the perfromance of the 'NaiveModel'
resultsDf = pd.concat([resultsDf,resultsDfN])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
For this particular simple average method, we will forecast by using the average of the training values.
# Let's create the copy of the train and test dataset
SimpleAverage_train = df_train.copy() # Complete the code to create a copy of the train datasets
SimpleAverage_test = df_test.copy() # Complete the code to create a copy of the test datasets
SimpleAverage_test['mean forecast']= df_train['Sparkling'].mean()
SimpleAverage_test.head()
| Sparkling | mean forecast | |
|---|---|---|
| Time_Stamp | ||
| 1992-01-31 | 1577 | 2408.930556 |
| 1992-02-29 | 1667 | 2408.930556 |
| 1992-03-31 | 1993 | 2408.930556 |
| 1992-04-30 | 1997 | 2408.930556 |
| 1992-05-31 | 1783 | 2408.930556 |
plt.figure(figsize=(15,12))
plt.plot(df_train['Sparkling'],label='Train')
plt.plot(df_test['Sparkling'], label='Test')
plt.plot(SimpleAverage_test['mean forecast'],label='Simple Average on Test Data')
plt.legend(loc='best')
plt.title("Simple Average Forecast")
plt.grid();
##MODEL EVAULATION
rmse_model_test = metrics.mean_squared_error(df_test['Sparkling'],SimpleAverage_test['mean forecast'],squared=False)
print("For Simple Average Forecast on Test Data, RMSE is %3.3f"%(rmse_model_test))
For Simple Average Forecast on Test Data, RMSE is 1268.683
resultsDfSES = pd.DataFrame({'Test RMSE': [rmse_model_test]},index=['SimpleAverageModel']) # Complete the code to check the perfromance of the 'SimpleAverageModel'
resultsDf = pd.concat([resultsDf,resultsDfSES])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
| SimpleAverageModel | 1268.683035 |
# Let's create the train and test dataset
SES_train = df_train.copy() # Complete the code to create a copy of the train datasets
SES_test = df_test.copy() # Complete the code to create a copy of the test datasets
model_SES = SimpleExpSmoothing(SES_train['Sparkling'])
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq)
model_SES_autofit = model_SES.fit(optimized=True)
model_SES_autofit.params
{'smoothing_level': 0.03953488372093023,
'smoothing_trend': nan,
'smoothing_seasonal': nan,
'damping_trend': nan,
'initial_level': 1686.0,
'initial_trend': nan,
'initial_seasons': array([], dtype=float64),
'use_boxcox': False,
'lamda': None,
'remove_bias': False}
SES_test['predict'] = model_SES_autofit.forecast(steps=len(df_test))
SES_test.head()
| Sparkling | predict | |
|---|---|---|
| Time_Stamp | ||
| 1992-01-31 | 1577 | 2648.540985 |
| 1992-02-29 | 1667 | 2648.540985 |
| 1992-03-31 | 1993 | 2648.540985 |
| 1992-04-30 | 1997 | 2648.540985 |
| 1992-05-31 | 1783 | 2648.540985 |
## Plotting on both the Training and Test data
plt.figure(figsize=(16,8))
plt.plot(SES_train['Sparkling'], label='Train')
plt.plot(SES_test['Sparkling'], label='Test')
plt.plot(SES_test['predict'], label='Alpha = 0.05 Simple Exponential Smoothing predictions on Test Set')
plt.legend(loc='best')
plt.grid()
plt.title('Alpha = 0.05 Predictions');
rmse_model_test = metrics.mean_squared_error(SES_test['Sparkling'],SES_test['predict'],squared=False)
print("For Alpha = 0.05 SES Model on Test Data,RMSE is %3.3f" %(rmse_model_test))
For Alpha = 0.05 SES Model on Test Data,RMSE is 1296.358
resultsDf_1 = pd.DataFrame({'Test RMSE': [rmse_model_test]},index=['SimpleExponentialSmoothing']) # Complete the code to check the perfromance of the 'Alpha = 0.05,SimpleExponentialSmoothing'
resultsDf = pd.concat([resultsDf, resultsDf_1])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
| SimpleAverageModel | 1268.683035 |
| SimpleExponentialSmoothing | 1296.358039 |
This result suggests that the SES model with 𝛼=0.05, performs slightly worse than the Simple Average Forecast (RMSE= 1268.683) but significantly better than the Naive Forecast (RMSE = 3979.915).
The RMSE of 1296.358 suggests that the SES model with 𝛼 = 0.05 performs moderately well, capturing the central tendency of the data but failing to adjust effectively to recent changes due to the low 𝛼.
Remember, the higher the alpha value more weightage is given to the more recent observation. That means, what happened recently will happen again. We will run a loop with different alpha values to understand which particular value works best for alpha on the test set.
First we will define an empty dataframe to store our values from the loop
resultsDf_a = pd.DataFrame({'Alpha Values':[],'Train RMSE':[],'Test RMSE': []})
resultsDf_a
| Alpha Values | Train RMSE | Test RMSE |
|---|
for i in np.arange(0.3,1,0.1):
model_SES_alpha_i = model_SES.fit(smoothing_level=i,optimized=False,use_brute=True)
SES_train['predict',i] = model_SES_alpha_i.fittedvalues
SES_test['predict',i] = model_SES_alpha_i.forecast(steps=55)
rmse_model5_train_i = metrics.mean_squared_error(SES_train['Sparkling'],SES_train['predict',i],squared=False)
rmse_model5_test_i = metrics.mean_squared_error(SES_test['Sparkling'],SES_test['predict',i],squared=False)
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
,'Test RMSE':rmse_model5_test_i}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
resultsDf_a.sort_values(by=['Test RMSE'],ascending=True)
| Alpha Values | Train RMSE | Test RMSE | |
|---|---|---|---|
| 0 | 0.3 | 1362.731346 | 1900.058569 |
| 1 | 0.4 | 1356.208919 | 2260.069389 |
| 2 | 0.5 | 1347.944758 | 2606.296390 |
| 3 | 0.6 | 1343.099607 | 2924.118301 |
| 4 | 0.7 | 1343.640190 | 3214.744366 |
| 5 | 0.8 | 1349.991473 | 3483.731806 |
| 6 | 0.9 | 1362.270034 | 3736.981096 |
## Plotting on both the Training and Test data
plt.figure(figsize=(18,9))
plt.plot(SES_train['Sparkling'], label='Train')
plt.plot(SES_test['Sparkling'], label='Test')
plt.plot(SES_test['predict'], label='Alpha = 0.05 Simple Exponential Smoothing predictions on Test Set')
plt.plot(SES_test['predict', 0.3], label='Alpha = 0.3 Simple Exponential Smoothing predictions on Test Set')
plt.legend(loc='best')
plt.grid();
resultsDf_2 = pd.DataFrame({'Test RMSE': [resultsDf_a.sort_values(by=['Test RMSE'],ascending=True).values[0][2]]}
,index=['SimpleExponentialSmoothing(α=0.3)']) # Complete the code to check the perfromance of the 'Alpha=0.3,SimpleExponentialSmoothing'
resultsDf = pd.concat([resultsDf, resultsDf_2])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
| SimpleAverageModel | 1268.683035 |
| SimpleExponentialSmoothing | 1296.358039 |
| SimpleExponentialSmoothing(α=0.3) | 1900.058569 |
Two parameters 𝛼 and 𝛽 are estimated in this model. Level and Trend are accounted for in this model.
DES_train = df_train.copy() # Complete the code to create a copy of the train dataset
DES_test =df_test.copy() # Complete the code to create a copy of the test dataset
model_DES = Holt(DES_train['Sparkling'])
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq)
model_DES_autofit = model_DES.fit()
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\holtwinters\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals. warnings.warn(
model_DES_autofit.params
{'smoothing_level': 0.6885714285714285,
'smoothing_trend': 9.999999999999999e-05,
'smoothing_seasonal': nan,
'damping_trend': nan,
'initial_level': 1686.0,
'initial_trend': -95.0,
'initial_seasons': array([], dtype=float64),
'use_boxcox': False,
'lamda': None,
'remove_bias': False}
DES_test.shape
(43, 1)
## Prediction on the test data
DES_test['auto_predict'] = model_DES_autofit.forecast(steps=55)
DES_test.head()
| Sparkling | auto_predict | |
|---|---|---|
| Time_Stamp | ||
| 1992-01-31 | 1577 | 5164.113674 |
| 1992-02-29 | 1667 | 5070.828157 |
| 1992-03-31 | 1993 | 4977.542640 |
| 1992-04-30 | 1997 | 4884.257122 |
| 1992-05-31 | 1783 | 4790.971605 |
## Plotting on both the Training and Test using autofit
plt.figure(figsize=(18,9))
plt.plot(DES_train['Sparkling'], label='Train')
plt.plot(DES_test['Sparkling'], label='Test')
plt.plot(DES_test['auto_predict'], label='Alpha = 0.688,Beta = 9.99e-05,DoubleExponentialSmoothing predictions on Test Set')
plt.legend(loc='best')
plt.grid();
## Test Data
rmse_model_test = metrics.mean_squared_error(DES_test['Sparkling'],DES_test['auto_predict'],squared=False)
print("For Alpha=0.688,Beta=0.00009,DoubleExponentialSmoothing predictions on Test Data, RMSE is %3.3f" %(rmse_model_test))
For Alpha=0.688,Beta=0.00009,DoubleExponentialSmoothing predictions on Test Data, RMSE is 1923.793
resultsDf_DES = pd.DataFrame({'Test RMSE': [rmse_model_test]}
,index=['DoubleExponentialSmoothing(α=0.688)']) # Complete the code to check the perfromance of the 'Alpha=0.688,Beta=0.00009,DoubleExponentialSmoothing'
resultsDf = pd.concat([resultsDf, resultsDf_DES])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
| SimpleAverageModel | 1268.683035 |
| SimpleExponentialSmoothing | 1296.358039 |
| SimpleExponentialSmoothing(α=0.3) | 1900.058569 |
| DoubleExponentialSmoothing(α=0.688) | 1923.793446 |
## First we will define an empty dataframe to store our values from the loop
resultsDf_b = pd.DataFrame({'Alpha Values':[],'Beta Values':[],'Train RMSE':[],'Test RMSE': []})
resultsDf_b
| Alpha Values | Beta Values | Train RMSE | Test RMSE |
|---|
len(df_test)
43
for i in np.arange(0.3,1.1,0.1):
for j in np.arange(0.3,1.1,0.1):
model_DES_alpha_i_j = model_DES.fit(smoothing_level=i,smoothing_trend=j,optimized=False,use_brute=True)
DES_train['predict',i,j] = model_DES_alpha_i_j.fittedvalues
DES_test['predict',i,j] = model_DES_alpha_i_j.forecast(steps=55)
rmse_model_train = metrics.mean_squared_error(DES_train['Sparkling'],DES_train['predict',i,j],squared=False)
rmse_model_test = metrics.mean_squared_error(DES_test['Sparkling'],DES_test['predict',i,j],squared=False)
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
,'Test RMSE':rmse_model_test}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
resultsDf_b.sort_values(by=['Test RMSE']).head()
| Alpha Values | Beta Values | Train RMSE | Test RMSE | |
|---|---|---|---|---|
| 0 | 0.3 | 0.3 | 1599.398181 | 13814.398308 |
| 8 | 0.4 | 0.3 | 1578.236948 | 18044.669982 |
| 1 | 0.3 | 0.4 | 1693.819564 | 19249.257218 |
| 16 | 0.5 | 0.3 | 1538.865716 | 20684.637333 |
| 24 | 0.6 | 0.3 | 1513.676637 | 22536.689632 |
DES_test.columns
Index([ 'Sparkling',
'auto_predict',
('predict', 0.3, 0.3),
('predict', 0.3, 0.4),
('predict', 0.3, 0.5),
('predict', 0.3, 0.6000000000000001),
('predict', 0.3, 0.7000000000000002),
('predict', 0.3, 0.8000000000000003),
('predict', 0.3, 0.9000000000000001),
('predict', 0.3, 1.0000000000000002),
('predict', 0.4, 0.3),
('predict', 0.4, 0.4),
('predict', 0.4, 0.5),
('predict', 0.4, 0.6000000000000001),
('predict', 0.4, 0.7000000000000002),
('predict', 0.4, 0.8000000000000003),
('predict', 0.4, 0.9000000000000001),
('predict', 0.4, 1.0000000000000002),
('predict', 0.5, 0.3),
('predict', 0.5, 0.4),
('predict', 0.5, 0.5),
('predict', 0.5, 0.6000000000000001),
('predict', 0.5, 0.7000000000000002),
('predict', 0.5, 0.8000000000000003),
('predict', 0.5, 0.9000000000000001),
('predict', 0.5, 1.0000000000000002),
('predict', 0.6000000000000001, 0.3),
('predict', 0.6000000000000001, 0.4),
('predict', 0.6000000000000001, 0.5),
('predict', 0.6000000000000001, 0.6000000000000001),
('predict', 0.6000000000000001, 0.7000000000000002),
('predict', 0.6000000000000001, 0.8000000000000003),
('predict', 0.6000000000000001, 0.9000000000000001),
('predict', 0.6000000000000001, 1.0000000000000002),
('predict', 0.7000000000000002, 0.3),
('predict', 0.7000000000000002, 0.4),
('predict', 0.7000000000000002, 0.5),
('predict', 0.7000000000000002, 0.6000000000000001),
('predict', 0.7000000000000002, 0.7000000000000002),
('predict', 0.7000000000000002, 0.8000000000000003),
('predict', 0.7000000000000002, 0.9000000000000001),
('predict', 0.7000000000000002, 1.0000000000000002),
('predict', 0.8000000000000003, 0.3),
('predict', 0.8000000000000003, 0.4),
('predict', 0.8000000000000003, 0.5),
('predict', 0.8000000000000003, 0.6000000000000001),
('predict', 0.8000000000000003, 0.7000000000000002),
('predict', 0.8000000000000003, 0.8000000000000003),
('predict', 0.8000000000000003, 0.9000000000000001),
('predict', 0.8000000000000003, 1.0000000000000002),
('predict', 0.9000000000000001, 0.3),
('predict', 0.9000000000000001, 0.4),
('predict', 0.9000000000000001, 0.5),
('predict', 0.9000000000000001, 0.6000000000000001),
('predict', 0.9000000000000001, 0.7000000000000002),
('predict', 0.9000000000000001, 0.8000000000000003),
('predict', 0.9000000000000001, 0.9000000000000001),
('predict', 0.9000000000000001, 1.0000000000000002),
('predict', 1.0000000000000002, 0.3),
('predict', 1.0000000000000002, 0.4),
('predict', 1.0000000000000002, 0.5),
('predict', 1.0000000000000002, 0.6000000000000001),
('predict', 1.0000000000000002, 0.7000000000000002),
('predict', 1.0000000000000002, 0.8000000000000003),
('predict', 1.0000000000000002, 0.9000000000000001),
('predict', 1.0000000000000002, 1.0000000000000002)],
dtype='object')
resultsDf_3 = pd.DataFrame({'Test RMSE': [resultsDf_b.sort_values(by=['Test RMSE']).values[0][3]]}
,index=['Alpha=0.6,Beta=0.0001,DoubleExponentialSmoothing']) # Complete the code to check the perfromance of the 'Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing'
resultsDf = pd.concat([resultsDf, resultsDf_3])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
| SimpleAverageModel | 1268.683035 |
| SimpleExponentialSmoothing | 1296.358039 |
| SimpleExponentialSmoothing(α=0.3) | 1900.058569 |
| DoubleExponentialSmoothing(α=0.688) | 1923.793446 |
| Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing | 13814.398308 |
| Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing | 13814.398308 |
Three parameters 𝛼 , 𝛽 and 𝛾 are estimated in this model. Level, Trend and Seasonality are accounted for in this model.
# Creating the copy of train and test dataset
TES_train = df_train.copy() # Complete the code to create a copy of the train dataset
TES_test = df_test.copy() # Complete the code to create a copy of the test dataset
model_TES = ExponentialSmoothing(TES_train['Sparkling'],trend='additive',seasonal='multiplicative',freq='M')
model_TES_autofit = model_TES.fit()
model_TES_autofit.params
{'smoothing_level': 0.0760775317017757,
'smoothing_trend': 0.07607728744840153,
'smoothing_seasonal': 0.34206210109518037,
'damping_trend': nan,
'initial_level': 2356.3687669747187,
'initial_trend': -16.605498759558397,
'initial_seasons': array([0.72013071, 0.68871014, 0.90372652, 0.80520439, 0.65572451,
0.65102787, 0.88120117, 1.13713477, 0.92418717, 1.22521121,
1.92183841, 2.44205244]),
'use_boxcox': False,
'lamda': None,
'remove_bias': False}
##PREDICTION ON TEST SET
TES_test['auto_predict'] = model_TES_autofit.forecast(steps=len(df_test))
TES_test.head()
| Sparkling | auto_predict | |
|---|---|---|
| Time_Stamp | ||
| 1992-01-31 | 1577 | 1720.327211 |
| 1992-02-29 | 1667 | 1612.401776 |
| 1992-03-31 | 1993 | 1794.921743 |
| 1992-04-30 | 1997 | 1531.035046 |
| 1992-05-31 | 1783 | 1508.819520 |
## Plotting on both the Training and Test using autofit
plt.figure(figsize=(18,9))
plt.plot(TES_train['Sparkling'], label='Train')
plt.plot(TES_test['Sparkling'], label='Test')
plt.plot(TES_test['auto_predict'], label='Alpha=0.111,Beta=0.061,Gamma=0.395,TripleExponentialSmoothing predictions on Test Set')
plt.legend(loc='best')
plt.grid();
## Test Data
rmse_model_test = metrics.mean_squared_error(TES_test['Sparkling'],TES_test['auto_predict'],squared=False)
print("Alpha=0.111,Beta=0.061,Gamma=0.395,TripleExponentialSmoothing predictions on Test Set, RMSE is %3.3f" %(rmse_model_test))
Alpha=0.111,Beta=0.061,Gamma=0.395,TripleExponentialSmoothing predictions on Test Set, RMSE is 347.415
## First we will define an empty dataframe to store our values from the loop
resultsDf_c = pd.DataFrame({'Alpha Values':[],'Beta Values':[],'Gamma Values':[],'Train RMSE':[],'Test RMSE': []})
resultsDf_c
| Alpha Values | Beta Values | Gamma Values | Train RMSE | Test RMSE |
|---|
for i in np.arange(0.3,1.1,0.1):
for j in np.arange(0.3,1.1,0.1):
for k in np.arange(0.3,1.1,0.1):
model_TES_alpha_i_j_k = model_TES.fit(smoothing_level=i,smoothing_trend=j,smoothing_seasonal=k,optimized=False,use_brute=True)
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
rmse_model_train = metrics.mean_squared_error(TES_train['Sparkling'],TES_train['predict',i,j,k],squared=False)
rmse_model_test = metrics.mean_squared_error(TES_test['Sparkling'],TES_test['predict',i,j,k],squared=False)
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
'Train RMSE':rmse_model_train,'Test RMSE':rmse_model_test}
, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
resultsDf_c.sort_values(by=['Test RMSE']).head()
| Alpha Values | Beta Values | Gamma Values | Train RMSE | Test RMSE | |
|---|---|---|---|---|---|
| 128 | 0.5 | 0.3 | 0.3 | 448.489627 | 412.613756 |
| 194 | 0.6 | 0.3 | 0.5 | 541.879579 | 478.752553 |
| 72 | 0.4 | 0.4 | 0.3 | 451.720449 | 485.608082 |
| 260 | 0.7 | 0.3 | 0.7 | 726.763349 | 561.382299 |
| 81 | 0.4 | 0.5 | 0.4 | 508.681964 | 579.245326 |
## Plotting on both the Training and Test data using brute force alpha, beta and gamma determination
plt.figure(figsize=(18,9))
plt.plot(TES_train['Sparkling'], label='Train')
plt.plot(TES_test['Sparkling'], label='Test')
#The value of alpha and beta is taken like that by python
plt.plot(TES_test['predict', 0.5, 0.3, 0.3], label='Alpha=0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing predictions on Test Set')
plt.legend(loc='best')
plt.grid();
resultsDf_2 = pd.DataFrame({'Test RMSE': [resultsDf_c.sort_values(by=['Test RMSE']).values[0][4]]}
,index=['Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing']) # Complete the code to check the perfromance of the 'Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing'
resultsDf = pd.concat([resultsDf, resultsDf_2])
resultsDf
| Test RMSE | |
|---|---|
| RegressionOnTime | 1337.090220 |
| NaiveModel | 3979.914692 |
| SimpleAverageModel | 1268.683035 |
| SimpleExponentialSmoothing | 1296.358039 |
| SimpleExponentialSmoothing(α=0.3) | 1900.058569 |
| DoubleExponentialSmoothing(α=0.688) | 1923.793446 |
| Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing | 13814.398308 |
| Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing | 13814.398308 |
| Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing | 412.613756 |
| Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing | 412.613756 |
resultsDf1 = resultsDf.sort_values(by=['Test RMSE'])
resultsDf1
| Test RMSE | |
|---|---|
| Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing | 412.613756 |
| Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing | 412.613756 |
| SimpleAverageModel | 1268.683035 |
| SimpleExponentialSmoothing | 1296.358039 |
| RegressionOnTime | 1337.090220 |
| SimpleExponentialSmoothing(α=0.3) | 1900.058569 |
| DoubleExponentialSmoothing(α=0.688) | 1923.793446 |
| NaiveModel | 3979.914692 |
| Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing | 13814.398308 |
| Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing | 13814.398308 |
Let's check for the stationarity of the data on which the model is being built on using appropriate statistical tests and also mention the hypothesis for the statistical test. If the data is found to be non-stationary, take appropriate steps to make it stationary. Check the new data for stationarity and comment. Note: Stationarity should be checked at alpha = 0.05.
from statsmodels.tsa.stattools import adfuller
def test_stationarity(timeseries):
#determining roll statistics
rolmean = timeseries.rolling(window=12).mean()
rolstd = timeseries.rolling(window=12).std()
##plot rolling Statistics:
orig = plt.plot(timeseries,color='blue',label='Original')
mean = plt.plot(rolmean, color='red', label='Rolling Mean')
std = plt.plot(rolstd, color='black', label='Rolling Std')
plt.legend(loc='best')
plt.title('Rolling Mean and Standard Deviation')
plt.show(block=False)
#Perform Dickey-Fuller Test:
print('Results of Dickey Fuller Test:')
dftest = adfuller(timeseries, autolag='AIC')
dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
for key,value in dftest[4].items():
dfoutput['Critical Value (%s)'%key] = value
print(dfoutput,'\n')
test_stationarity(df_train['Sparkling'])
Results of Dickey Fuller Test: Test Statistic -1.265771 p-value 0.644683 #Lags Used 12.000000 Number of Observations Used 131.000000 Critical Value (1%) -3.481282 Critical Value (5%) -2.883868 Critical Value (10%) -2.578677 dtype: float64
Write the Observations
H0 : The series is not stationary
Ha: The series is Stationary
Note:
autolag{“AIC”, “BIC”, “t-stat”, None} Method to use when automatically determining the lag length among the values 0, 1, …, maxlag.
If “AIC” (default) or “BIC”, then the number of lags is chosen to minimize the corresponding information criterion.
“t-stat” based choice of maxlag. Starts with maxlag and drops a lag until the t-statistic on the last lag length is significant using a 5%-sized test.
If None, then the number of included lags is set to maxlag.
Stationary TS allow us to essentially have copies of things which enables us to build appropriate statistical models for forecasting
LETS BUILD IT ON DF_TRAIN MODEL
dftest = adfuller(df_train.diff().dropna(),regression='ct')
print('DF test statistics is %3.3f' %dftest[0])
print('DF test p-value is', dftest[1])
print('DF test p-value is', dftest[2])
DF test statistics is -8.628 DF test p-value is 2.5490597847951888e-12 DF test p-value is 11
df_train.plot(grid=True);
Let's build an automated version of the ARIMA model in which the parameters are selected using the lowest Akaike Information Criteria (AIC) on the training data and evaluate this model on the test data using RMSE.
import itertools
p = q = range(0,4)
d = range(1,2)
pdq = list(itertools.product(p,d,q))
print('Examples of the parameter combinations for the models')
for i in range(0,len(pdq)):
print('Model : {}'.format(pdq[i]))
Examples of the parameter combinations for the models Model : (0, 1, 0) Model : (0, 1, 1) Model : (0, 1, 2) Model : (0, 1, 3) Model : (1, 1, 0) Model : (1, 1, 1) Model : (1, 1, 2) Model : (1, 1, 3) Model : (2, 1, 0) Model : (2, 1, 1) Model : (2, 1, 2) Model : (2, 1, 3) Model : (3, 1, 0) Model : (3, 1, 1) Model : (3, 1, 2) Model : (3, 1, 3)
# Creating an empty Dataframe with column names only
ARIMA_AIC = pd.DataFrame(columns=['param', 'AIC'])
ARIMA_AIC
| param | AIC |
|---|
from statsmodels.tsa.arima.model import ARIMA
for param in pdq: # running a loop within the pdq parameters defined by itertools
ARIMA_model = ARIMA(df_train['Sparkling'].values, order=param).fit()
print('ARIMA{} - AIC{}'.format(param, ARIMA_model.aic))
#printing the parameters and the AIC from the fitted models
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
#appending the AIC values and the model parameters to the previously created data frame
#for easier understanding and sorting of the AIC values
ARIMA(0, 1, 0) - AIC2476.745546947 ARIMA(0, 1, 1) - AIC2470.9032654144103
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(0, 1, 2) - AIC2439.4718189796276 ARIMA(0, 1, 3) - AIC2439.127183835587
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(1, 1, 0) - AIC2474.923642766008 ARIMA(1, 1, 1) - AIC2440.4861127564113
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(1, 1, 2) - AIC2439.712391776987
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(1, 1, 3) - AIC2440.808627826275 ARIMA(2, 1, 0) - AIC2468.7139871180234
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(2, 1, 1) - AIC2438.8717349509843
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(2, 1, 2) - AIC2419.166998310233
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA(2, 1, 3) - AIC2438.3644928591807 ARIMA(3, 1, 0) - AIC2466.346553693917 ARIMA(3, 1, 1) - AIC2440.4277709016706
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
ARIMA(3, 1, 2) - AIC2435.456935737263 ARIMA(3, 1, 3) - AIC2427.5078516769345
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
ARIMA_AIC.sort_values(by='AIC',ascending=True).head()
| param | AIC | |
|---|---|---|
| 10 | (2, 1, 2) | 2419.166998 |
| 15 | (3, 1, 3) | 2427.507852 |
| 14 | (3, 1, 2) | 2435.456936 |
| 11 | (2, 1, 3) | 2438.364493 |
| 9 | (2, 1, 1) | 2438.871735 |
auto_ARIMA = ARIMA(df_train, order=(2,1,2))
results_auto_ARIMA = auto_ARIMA.fit()
print(results_auto_ARIMA.summary())
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq) C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq) C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: Sparkling No. Observations: 144
Model: ARIMA(2, 1, 2) Log Likelihood -1204.583
Date: Sun, 19 Jan 2025 AIC 2419.167
Time: 10:52:45 BIC 2433.981
Sample: 01-31-1980 HQIC 2425.187
- 12-31-1991
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 1.3214 0.043 30.410 0.000 1.236 1.407
ar.L2 -0.5501 0.062 -8.906 0.000 -0.671 -0.429
ma.L1 -1.9912 0.105 -18.931 0.000 -2.197 -1.785
ma.L2 0.9995 0.106 9.473 0.000 0.793 1.206
sigma2 1.136e+06 1.88e-07 6.05e+12 0.000 1.14e+06 1.14e+06
===================================================================================
Ljung-Box (L1) (Q): 0.07 Jarque-Bera (JB): 15.12
Prob(Q): 0.80 Prob(JB): 0.00
Heteroskedasticity (H): 2.03 Skew: 0.61
Prob(H) (two-sided): 0.02 Kurtosis: 4.03
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.27e+28. Standard errors may be unstable.
results_auto_ARIMA.plot_diagnostics()
Predict on the Test Set using this model and evaluate the model.
predicted_auto_ARIMA = results_auto_ARIMA.forecast(steps=len(df_test))
## Mean Absolute Percentage Error (MAPE) - Function Definition
def mean_absolute_percentage_error(y_true, y_pred):
return np.mean((np.abs(y_true-y_pred))/(y_true))*100
## Importing the mean_squared_error function from sklearn to calculate the RMSE
from sklearn.metrics import mean_squared_error
rmse = mean_squared_error(df_test['Sparkling'],predicted_auto_ARIMA,squared=False)
mape = mean_absolute_percentage_error(df_test['Sparkling'],predicted_auto_ARIMA)
print('RMSE:',rmse,'\nMAPE:',mape)
RMSE: 1309.6316772059226 MAPE: 42.106202366124656
from math import sqrt
from sklearn.metrics import mean_squared_error
rmse = sqrt(mean_squared_error(df_test.Sparkling,predicted_auto_ARIMA))
print(rmse)
1309.6316772059226
resultsDf = pd.DataFrame({'RMSE': rmse,'MAPE':mape}
,index=['ARIMA(2,1,2)'])
resultsDf
| RMSE | MAPE | |
|---|---|---|
| ARIMA(2,1,2) | 1309.631677 | 42.106202 |
Let's build an automated version of the SARIMA model in which the parameters are selected using the lowest Akaike Information Criteria (AIC) on the training data and evaluate this model on the test data using RMSE.
plot_acf(df_train.diff(),title='Training Data Autocorrelation',missing='drop',lags=40);
import itertools
p = q = range(0, 4)
d= range(1,2)
D = range(0,1)
pdq = list(itertools.product(p, d, q))
PDQ = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, D, q))]
print('Examples of the parameter combinations for the Model are')
for i in range(1,len(pdq)):
print('Model: {}{}'.format(pdq[i], PDQ[i]))
Examples of the parameter combinations for the Model are Model: (0, 1, 1)(0, 0, 1, 12) Model: (0, 1, 2)(0, 0, 2, 12) Model: (0, 1, 3)(0, 0, 3, 12) Model: (1, 1, 0)(1, 0, 0, 12) Model: (1, 1, 1)(1, 0, 1, 12) Model: (1, 1, 2)(1, 0, 2, 12) Model: (1, 1, 3)(1, 0, 3, 12) Model: (2, 1, 0)(2, 0, 0, 12) Model: (2, 1, 1)(2, 0, 1, 12) Model: (2, 1, 2)(2, 0, 2, 12) Model: (2, 1, 3)(2, 0, 3, 12) Model: (3, 1, 0)(3, 0, 0, 12) Model: (3, 1, 1)(3, 0, 1, 12) Model: (3, 1, 2)(3, 0, 2, 12) Model: (3, 1, 3)(3, 0, 3, 12)
SARIMA_AIC = pd.DataFrame(columns=['param','seasonal', 'AIC'])
SARIMA_AIC
| param | seasonal | AIC |
|---|
import statsmodels.api as sm
for param in pdq:
for param_seasonal in PDQ:
SARIMA_model = sm.tsa.statespace.SARIMAX(df_train['Sparkling'].values,
order=param,
seasonal_order=param_seasonal,
enforce_stationarity=False,
enforce_invertibility=False)
results_SARIMA = SARIMA_model.fit(maxiter=1000)
print('SARIMA{}x{} - AIC:{}'.format(param, param_seasonal, results_SARIMA.aic))
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(0, 0, 0, 12) - AIC:2460.4391566410877 SARIMA(0, 1, 0)x(0, 0, 1, 12) - AIC:2153.4499863142355
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(0, 0, 2, 12) - AIC:1913.3324130187623
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMA(0, 1, 0)x(0, 0, 3, 12) - AIC:7736.558286878177 SARIMA(0, 1, 0)x(1, 0, 0, 12) - AIC:2019.7175595892088 SARIMA(0, 1, 0)x(1, 0, 1, 12) - AIC:1989.778581372794
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(1, 0, 2, 12) - AIC:1814.4132836398865
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(1, 0, 3, 12) - AIC:3170.2310499644864 SARIMA(0, 1, 0)x(2, 0, 0, 12) - AIC:1830.0102939478718 SARIMA(0, 1, 0)x(2, 0, 1, 12) - AIC:1830.0003655676471 SARIMA(0, 1, 0)x(2, 0, 2, 12) - AIC:1815.8048034159779
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(2, 0, 3, 12) - AIC:3925.930246833829 SARIMA(0, 1, 0)x(3, 0, 0, 12) - AIC:1651.6072552741205 SARIMA(0, 1, 0)x(3, 0, 1, 12) - AIC:1653.4767876481185
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(3, 0, 2, 12) - AIC:1654.8722679500472
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 0)x(3, 0, 3, 12) - AIC:4248.639442505446 SARIMA(0, 1, 1)x(0, 0, 0, 12) - AIC:2437.9469723862794 SARIMA(0, 1, 1)x(0, 0, 1, 12) - AIC:2122.815214759641
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(0, 0, 2, 12) - AIC:1879.9340913721007
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(0, 0, 3, 12) - AIC:7666.628947422329 SARIMA(0, 1, 1)x(1, 0, 0, 12) - AIC:1972.078873375368
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(1, 0, 1, 12) - AIC:1917.6448104928375 SARIMA(0, 1, 1)x(1, 0, 2, 12) - AIC:1749.380573458138
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(1, 0, 3, 12) - AIC:3228.7139413047853 SARIMA(0, 1, 1)x(2, 0, 0, 12) - AIC:1782.2937723723971
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(2, 0, 1, 12) - AIC:1778.614013015443
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(2, 0, 2, 12) - AIC:1750.0719103461688
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(2, 0, 3, 12) - AIC:3423.719077320342 SARIMA(0, 1, 1)x(3, 0, 0, 12) - AIC:1606.8038578319574
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(3, 0, 1, 12) - AIC:1608.4054072890776
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(3, 0, 2, 12) - AIC:1609.4754899003722
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 1)x(3, 0, 3, 12) - AIC:3427.6911885866393 SARIMA(0, 1, 2)x(0, 0, 0, 12) - AIC:2392.7436964785766 SARIMA(0, 1, 2)x(0, 0, 1, 12) - AIC:2086.229485518489
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(0, 0, 2, 12) - AIC:1847.1669919492779
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(0, 0, 3, 12) - AIC:7592.594051185987 SARIMA(0, 1, 2)x(1, 0, 0, 12) - AIC:1964.4202716174445
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(1, 0, 1, 12) - AIC:1901.3324565638677
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(1, 0, 2, 12) - AIC:1733.9753420990967
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(1, 0, 3, 12) - AIC:3865.8479597535134 SARIMA(0, 1, 2)x(2, 0, 0, 12) - AIC:1777.8175657592794
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(2, 0, 1, 12) - AIC:1777.172895358724
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(2, 0, 2, 12) - AIC:1734.445405430158
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(2, 0, 3, 12) - AIC:4071.439172329453 SARIMA(0, 1, 2)x(3, 0, 0, 12) - AIC:1604.4943869311664
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(3, 0, 1, 12) - AIC:1606.3471395952924
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(3, 0, 2, 12) - AIC:1607.9857271054689
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 2)x(3, 0, 3, 12) - AIC:4090.7735468714886 SARIMA(0, 1, 3)x(0, 0, 0, 12) - AIC:2374.6776328692195 SARIMA(0, 1, 3)x(0, 0, 1, 12) - AIC:2072.0398134889556
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(0, 0, 2, 12) - AIC:1831.9543283494784
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(0, 0, 3, 12) - AIC:2560.692642479879 SARIMA(0, 1, 3)x(1, 0, 0, 12) - AIC:1966.3365287368124
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(1, 0, 1, 12) - AIC:1887.3250749955146
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(1, 0, 2, 12) - AIC:1719.120791325296
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(1, 0, 3, 12) - AIC:3671.393770543239 SARIMA(0, 1, 3)x(2, 0, 0, 12) - AIC:1778.5020512955862
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(2, 0, 1, 12) - AIC:1777.5814650644572
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(2, 0, 2, 12) - AIC:1719.9490454184013
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(2, 0, 3, 12) - AIC:3867.0679254891743 SARIMA(0, 1, 3)x(3, 0, 0, 12) - AIC:1604.840596891594
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(3, 0, 1, 12) - AIC:1615.1376510495127
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(3, 0, 2, 12) - AIC:1607.8959651220775
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(0, 1, 3)x(3, 0, 3, 12) - AIC:3869.0109336901696 SARIMA(1, 1, 0)x(0, 0, 0, 12) - AIC:2458.624148794397 SARIMA(1, 1, 0)x(0, 0, 1, 12) - AIC:2151.913471416122 SARIMA(1, 1, 0)x(0, 0, 2, 12) - AIC:1910.748419991556
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(0, 0, 3, 12) - AIC:7740.669177833487 SARIMA(1, 1, 0)x(1, 0, 0, 12) - AIC:1992.391512768205 SARIMA(1, 1, 0)x(1, 0, 1, 12) - AIC:1972.5427510742556
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(1, 0, 2, 12) - AIC:1800.7835498683557
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(1, 0, 3, 12) - AIC:3956.0104227365946 SARIMA(1, 1, 0)x(2, 0, 0, 12) - AIC:1803.4501723395315 SARIMA(1, 1, 0)x(2, 0, 1, 12) - AIC:1801.4441167811785
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(2, 0, 2, 12) - AIC:1801.1304633575191
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(2, 0, 3, 12) - AIC:4156.161078096781 SARIMA(1, 1, 0)x(3, 0, 0, 12) - AIC:1625.5224208200887
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(3, 0, 1, 12) - AIC:1627.4151709288737
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(3, 0, 2, 12) - AIC:1628.9691474768551
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 0)x(3, 0, 3, 12) - AIC:4159.295083838122 SARIMA(1, 1, 1)x(0, 0, 0, 12) - AIC:2410.0884627275573 SARIMA(1, 1, 1)x(0, 0, 1, 12) - AIC:2104.0129508091877
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(0, 0, 2, 12) - AIC:1864.1374831824196
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(0, 0, 3, 12) - AIC:3371.938660800721 SARIMA(1, 1, 1)x(1, 0, 0, 12) - AIC:1949.8127631915477 SARIMA(1, 1, 1)x(1, 0, 1, 12) - AIC:1917.6915508145426
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(1, 0, 2, 12) - AIC:1748.9115708430454
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(1, 0, 3, 12) - AIC:2741.138894121306 SARIMA(1, 1, 1)x(2, 0, 0, 12) - AIC:1765.438703039601
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(2, 0, 1, 12) - AIC:1821.524510286976
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(2, 0, 2, 12) - AIC:1749.6504531323712
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(2, 0, 3, 12) - AIC:3395.103925062856 SARIMA(1, 1, 1)x(3, 0, 0, 12) - AIC:1591.6112831969194
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(3, 0, 1, 12) - AIC:1593.4430502620164
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(3, 0, 2, 12) - AIC:1595.0884305488437
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 1)x(3, 0, 3, 12) - AIC:3968.681048006381 SARIMA(1, 1, 2)x(0, 0, 0, 12) - AIC:2389.7064800921275 SARIMA(1, 1, 2)x(0, 0, 1, 12) - AIC:2087.9254267192737
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(0, 0, 2, 12) - AIC:1846.4490800555457
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(0, 0, 3, 12) - AIC:2488.4218970691054 SARIMA(1, 1, 2)x(1, 0, 0, 12) - AIC:1945.4280054907117
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(1, 0, 1, 12) - AIC:1900.84400774599
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(1, 0, 2, 12) - AIC:1732.0347524447334
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(1, 0, 3, 12) - AIC:2816.942205274622 SARIMA(1, 1, 2)x(2, 0, 0, 12) - AIC:1762.3269012066187
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(2, 0, 1, 12) - AIC:1761.8316809174598
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(2, 0, 2, 12) - AIC:1732.944772558156
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(2, 0, 3, 12) - AIC:2290.535003712238 SARIMA(1, 1, 2)x(3, 0, 0, 12) - AIC:1589.3127233303567
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(3, 0, 1, 12) - AIC:1591.2745630063318
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(3, 0, 2, 12) - AIC:1592.9373880966543
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 2)x(3, 0, 3, 12) - AIC:3644.326322619033 SARIMA(1, 1, 3)x(0, 0, 0, 12) - AIC:2374.9830777484476
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(0, 0, 1, 12) - AIC:2069.1041460100478
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(0, 0, 2, 12) - AIC:1827.841110058576
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(0, 0, 3, 12) - AIC:7513.610346303478 SARIMA(1, 1, 3)x(1, 0, 0, 12) - AIC:1947.46225827556
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(1, 0, 1, 12) - AIC:1888.8566118155038
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(1, 0, 2, 12) - AIC:1719.188841045211
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(1, 0, 3, 12) - AIC:3206.924513251405 SARIMA(1, 1, 3)x(2, 0, 0, 12) - AIC:1764.25730837763
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(2, 0, 1, 12) - AIC:1763.7728383498068
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(2, 0, 2, 12) - AIC:1720.3195534113383
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(2, 0, 3, 12) - AIC:3821.3283089046326 SARIMA(1, 1, 3)x(3, 0, 0, 12) - AIC:1591.3120545792917
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(3, 0, 1, 12) - AIC:1593.1380111556246
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(3, 0, 2, 12) - AIC:1594.9453586143
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(1, 1, 3)x(3, 0, 3, 12) - AIC:3742.9364414141023 SARIMA(2, 1, 0)x(0, 0, 0, 12) - AIC:2435.649959372059 SARIMA(2, 1, 0)x(0, 0, 1, 12) - AIC:2144.874957709288
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(0, 0, 2, 12) - AIC:1901.4803235044517
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(0, 0, 3, 12) - AIC:7736.941951598704 SARIMA(2, 1, 0)x(1, 0, 0, 12) - AIC:1961.619609298185 SARIMA(2, 1, 0)x(1, 0, 1, 12) - AIC:1941.6111848832134
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(1, 0, 2, 12) - AIC:1784.966733101205
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(1, 0, 3, 12) - AIC:3725.044374677721 SARIMA(2, 1, 0)x(2, 0, 0, 12) - AIC:1774.1144710902195
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(2, 0, 1, 12) - AIC:1772.344666819349
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(2, 0, 2, 12) - AIC:1770.7613838598343
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(2, 0, 3, 12) - AIC:3914.313552384413
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(3, 0, 0, 12) - AIC:1596.94605368483
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(3, 0, 1, 12) - AIC:1598.62139316676
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(3, 0, 2, 12) - AIC:1599.1944829406445
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 0)x(3, 0, 3, 12) - AIC:3597.1285068684138 SARIMA(2, 1, 1)x(0, 0, 0, 12) - AIC:2408.429360384621 SARIMA(2, 1, 1)x(0, 0, 1, 12) - AIC:2103.5331430917645
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(0, 0, 2, 12) - AIC:1862.2497960981998
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(0, 0, 3, 12) - AIC:14.0 SARIMA(2, 1, 1)x(1, 0, 0, 12) - AIC:1973.6400738430707
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(1, 0, 1, 12) - AIC:1917.209385632076
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(1, 0, 2, 12) - AIC:1748.7504631345582
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(1, 0, 3, 12) - AIC:3394.8512472880407 SARIMA(2, 1, 1)x(2, 0, 0, 12) - AIC:1751.698566258214
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(2, 0, 1, 12) - AIC:1750.0341243344562
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(2, 0, 2, 12) - AIC:1749.3790845701224
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(2, 0, 3, 12) - AIC:3373.2981317770855 SARIMA(2, 1, 1)x(3, 0, 0, 12) - AIC:1578.2212367924762
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(3, 0, 1, 12) - AIC:1580.2109506811382
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(3, 0, 2, 12) - AIC:1601.173132966918
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 1)x(3, 0, 3, 12) - AIC:4413.585516186368 SARIMA(2, 1, 2)x(0, 0, 0, 12) - AIC:2370.982698358752
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(0, 0, 1, 12) - AIC:2088.1841782032157
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(0, 0, 2, 12) - AIC:1848.4427820531214
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(0, 0, 3, 12) - AIC:16.0 SARIMA(2, 1, 2)x(1, 0, 0, 12) - AIC:1931.137047876182
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(1, 0, 1, 12) - AIC:1902.8131908163205
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(1, 0, 2, 12) - AIC:1734.032674177344
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(1, 0, 3, 12) - AIC:3640.940092994663 SARIMA(2, 1, 2)x(2, 0, 0, 12) - AIC:1750.2422707699332
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(2, 0, 1, 12) - AIC:1750.0164284403816
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(2, 0, 2, 12) - AIC:1734.9396244780244
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(2, 0, 3, 12) - AIC:3893.331610057794 SARIMA(2, 1, 2)x(3, 0, 0, 12) - AIC:1616.85040125673
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(3, 0, 1, 12) - AIC:1578.941826286308
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(3, 0, 2, 12) - AIC:1580.7015447569977
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 2)x(3, 0, 3, 12) - AIC:3728.648962206613 SARIMA(2, 1, 3)x(0, 0, 0, 12) - AIC:2377.957378876878 SARIMA(2, 1, 3)x(0, 0, 1, 12) - AIC:2060.1533591980315
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(0, 0, 2, 12) - AIC:1823.6483303976254
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(0, 0, 3, 12) - AIC:18.0 SARIMA(2, 1, 3)x(1, 0, 0, 12) - AIC:1932.9344057684111
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(1, 0, 1, 12) - AIC:1885.8990792803738
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(1, 0, 2, 12) - AIC:1717.157576965034
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(1, 0, 3, 12) - AIC:3863.637849053858 SARIMA(2, 1, 3)x(2, 0, 0, 12) - AIC:1751.9524243152648
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(2, 0, 1, 12) - AIC:1754.0395054664202
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(2, 0, 2, 12) - AIC:1733.9766562512843
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(2, 0, 3, 12) - AIC:3467.2467193554735 SARIMA(2, 1, 3)x(3, 0, 0, 12) - AIC:1581.276388355275
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(3, 0, 1, 12) - AIC:1580.6761970278346
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(3, 0, 2, 12) - AIC:1582.5716561422398
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(2, 1, 3)x(3, 0, 3, 12) - AIC:4212.546033129062 SARIMA(3, 1, 0)x(0, 0, 0, 12) - AIC:2417.0123227002114 SARIMA(3, 1, 0)x(0, 0, 1, 12) - AIC:2144.7897942267864
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(0, 0, 2, 12) - AIC:1899.147023053573
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(0, 0, 3, 12) - AIC:7736.3791965710825 SARIMA(3, 1, 0)x(1, 0, 0, 12) - AIC:1943.0539710381574
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(1, 0, 1, 12) - AIC:1924.150443462501
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(1, 0, 2, 12) - AIC:1783.3322075435085
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(1, 0, 3, 12) - AIC:3540.0560590766618 SARIMA(3, 1, 0)x(2, 0, 0, 12) - AIC:1759.5124321044048
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(2, 0, 1, 12) - AIC:1756.8874659732123
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(2, 0, 2, 12) - AIC:1755.5405791249955
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(2, 0, 3, 12) - AIC:4004.8861086781358 SARIMA(3, 1, 0)x(3, 0, 0, 12) - AIC:1580.849777004902
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(3, 0, 1, 12) - AIC:1582.2409475943919
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(3, 0, 2, 12) - AIC:1582.4254490586072
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 0)x(3, 0, 3, 12) - AIC:3664.625012154448 SARIMA(3, 1, 1)x(0, 0, 0, 12) - AIC:2390.310360066785
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(0, 0, 1, 12) - AIC:2105.4373468094345
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(0, 0, 2, 12) - AIC:1864.202673264885
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(0, 0, 3, 12) - AIC:7666.33201230038 SARIMA(3, 1, 1)x(1, 0, 0, 12) - AIC:1920.56576124557
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(1, 0, 1, 12) - AIC:1904.4321878979636
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(1, 0, 2, 12) - AIC:1750.576070219919
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(1, 0, 3, 12) - AIC:3186.609643705996 SARIMA(3, 1, 1)x(2, 0, 0, 12) - AIC:1739.4368150027497
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(2, 0, 1, 12) - AIC:1737.9669542312336
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(2, 0, 2, 12) - AIC:1761.060158837461
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(2, 0, 3, 12) - AIC:4427.734327424761 SARIMA(3, 1, 1)x(3, 0, 0, 12) - AIC:1563.9116233111158
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(3, 0, 1, 12) - AIC:1565.5617140741576
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(3, 0, 2, 12) - AIC:1566.5823059906488
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 1)x(3, 0, 3, 12) - AIC:2735.745046173969 SARIMA(3, 1, 2)x(0, 0, 0, 12) - AIC:2392.1921725797465
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(0, 0, 1, 12) - AIC:2085.217174356195
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(0, 0, 2, 12) - AIC:1849.3292775881957
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(0, 0, 3, 12) - AIC:1181.4956729014134 SARIMA(3, 1, 2)x(1, 0, 0, 12) - AIC:1918.9458282693686
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(1, 0, 1, 12) - AIC:1904.8144719564857
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(1, 0, 2, 12) - AIC:1736.0016351448617
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(1, 0, 3, 12) - AIC:2256.8074479242377 SARIMA(3, 1, 2)x(2, 0, 0, 12) - AIC:1738.320379909521
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(2, 0, 1, 12) - AIC:1737.2517825868829
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(2, 0, 2, 12) - AIC:1736.930169588231
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(2, 0, 3, 12) - AIC:2315.05127794128 SARIMA(3, 1, 2)x(3, 0, 0, 12) - AIC:1563.137870783618
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(3, 0, 1, 12) - AIC:1565.0292067337928
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(3, 0, 2, 12) - AIC:1566.7896372438552
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 2)x(3, 0, 3, 12) - AIC:2317.0750458271464
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(0, 0, 0, 12) - AIC:2354.174527275618
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(0, 0, 1, 12) - AIC:2070.5911262733666
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(0, 0, 2, 12) - AIC:1835.0340101951513
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(0, 0, 3, 12) - AIC:7511.6310689813445
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(1, 0, 0, 12) - AIC:1920.8088506948184
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(1, 0, 1, 12) - AIC:1892.7715860422315
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(1, 0, 2, 12) - AIC:1722.2106722042117
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(1, 0, 3, 12) - AIC:3343.161719416555
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(2, 0, 0, 12) - AIC:1739.9796307502525
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(2, 0, 1, 12) - AIC:1737.8460282756726
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(2, 0, 2, 12) - AIC:1724.2062260580828
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(2, 0, 3, 12) - AIC:3083.9484080995217 SARIMA(3, 1, 3)x(3, 0, 0, 12) - AIC:1565.0970880236575
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(3, 0, 1, 12) - AIC:1567.0070121749645
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(3, 0, 2, 12) - AIC:1571.2183145544595
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA(3, 1, 3)x(3, 0, 3, 12) - AIC:3408.767325414378
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
SARIMA_AIC.sort_values(by='AIC',ascending=True)
| param | seasonal | AIC | |
|---|---|---|---|
| 147 | (2, 1, 1) | (0, 0, 3, 12) | 14.000000 |
| 163 | (2, 1, 2) | (0, 0, 3, 12) | 16.000000 |
| 179 | (2, 1, 3) | (0, 0, 3, 12) | 18.000000 |
| 227 | (3, 1, 2) | (0, 0, 3, 12) | 1181.495673 |
| 236 | (3, 1, 2) | (3, 0, 0, 12) | 1563.137871 |
| ... | ... | ... | ... |
| 19 | (0, 1, 1) | (0, 0, 3, 12) | 7666.628947 |
| 195 | (3, 1, 0) | (0, 0, 3, 12) | 7736.379197 |
| 3 | (0, 1, 0) | (0, 0, 3, 12) | 7736.558287 |
| 131 | (2, 1, 0) | (0, 0, 3, 12) | 7736.941952 |
| 67 | (1, 1, 0) | (0, 0, 3, 12) | 7740.669178 |
256 rows × 3 columns
import statsmodels.api as sm
auto_SARIMA = sm.tsa.statespace.SARIMAX(df_train['Sparkling'],order=(3,1,2),seasonal_order=(1,0,3,12),enforce_stationarity=False,
enforce_invertibility=False)
results_auto_SARIMA = auto_SARIMA.fit(maxiter=1000)
print(results_auto_SARIMA.summary())
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq) C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used. self._init_dates(dates, freq)
SARIMAX Results
==================================================================================================
Dep. Variable: Sparkling No. Observations: 144
Model: SARIMAX(3, 1, 2)x(1, 0, [1, 2, 3], 12) Log Likelihood -1118.404
Date: Sun, 19 Jan 2025 AIC 2256.807
Time: 11:44:23 BIC 2283.251
Sample: 01-31-1980 HQIC 2267.521
- 12-31-1991
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0985 11.158 -0.098 0.922 -22.967 20.770
ar.L2 0.2057 624.195 0.000 1.000 -1223.195 1223.606
ar.L3 -0.2747 214.364 -0.001 0.999 -420.420 419.870
ma.L1 0.8415 0.234 3.596 0.000 0.383 1.300
ma.L2 -0.7274 0.004 -196.158 0.000 -0.735 -0.720
ar.S.L12 0.9089 7.202 0.126 0.900 -13.207 15.024
ma.S.L12 -4.717e+12 1.27e-07 -3.72e+19 0.000 -4.72e+12 -4.72e+12
ma.S.L24 4.664e+12 1.22e-10 3.83e+22 0.000 4.66e+12 4.66e+12
ma.S.L36 -4.342e+12 6.13e-12 -7.09e+23 0.000 -4.34e+12 -4.34e+12
sigma2 1.779e+06 0.183 9.7e+06 0.000 1.78e+06 1.78e+06
===================================================================================
Ljung-Box (L1) (Q): 9.16 Jarque-Bera (JB): 5691.40
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.00 Skew: 5.23
Prob(H) (two-sided): 0.00 Kurtosis: 37.70
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.13e+44. Standard errors may be unstable.
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
results_auto_SARIMA.plot_diagnostics();
#Predict on the Test Set using this model and evaluate the model.
predicted_auto_SARIMA = results_auto_SARIMA.get_forecast(steps=len(df_test))
predicted_auto_SARIMA.summary_frame(alpha=0.05).head()
| Sparkling | mean | mean_se | mean_ci_lower | mean_ci_upper |
|---|---|---|---|---|
| 1992-01-31 | -2.352961e+22 | NaN | NaN | NaN |
| 1992-02-29 | 3.268265e+22 | NaN | NaN | NaN |
| 1992-03-31 | -4.539623e+22 | NaN | NaN | NaN |
| 1992-04-30 | 6.305540e+22 | NaN | NaN | NaN |
| 1992-05-31 | -8.758400e+22 | NaN | NaN | NaN |
rmse = mean_squared_error(df_test['Sparkling'],predicted_auto_SARIMA.predicted_mean,squared=False)
mape = mean_absolute_percentage_error(df_test['Sparkling'],predicted_auto_SARIMA.predicted_mean)
print('RMSE:',rmse,'\nMAPE:',mape)
RMSE: 5.093366358404006e+27 MAPE: 1.0559610841399717e+26
temp_resultsDf = pd.DataFrame({'RMSE': rmse,'MAPE':mape}
,index=['SARIMA(2,1,3)(1,0,3,12)'])
resultsDf = pd.concat([resultsDf,temp_resultsDf])
resultsDf
| RMSE | MAPE | |
|---|---|---|
| ARIMA(2,1,2) | 1.309632e+03 | 4.210620e+01 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 | 1.055961e+26 |
resultsDf2 = pd.concat([resultsDf,temp_resultsDf])
resultsDf2
| RMSE | MAPE | |
|---|---|---|
| ARIMA(2,1,2) | 1.309632e+03 | 4.210620e+01 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 | 1.055961e+26 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 | 1.055961e+26 |
resultsDf3 = resultsDf2[['RMSE']]
resultsDf3.rename(columns = { 'RMSE' : 'Test RMSE'}, inplace = True)
resultsDf3
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1466318677.py:2: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
resultsDf3.rename(columns = { 'RMSE' : 'Test RMSE'}, inplace = True)
| Test RMSE | |
|---|---|
| ARIMA(2,1,2) | 1.309632e+03 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 |
comparison_model_table = pd.concat([resultsDf1, resultsDf3])
comparison_model_table.sort_values(by = 'Test RMSE')
| Test RMSE | |
|---|---|
| Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing | 4.126138e+02 |
| Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing | 4.126138e+02 |
| SimpleAverageModel | 1.268683e+03 |
| SimpleExponentialSmoothing | 1.296358e+03 |
| ARIMA(2,1,2) | 1.309632e+03 |
| RegressionOnTime | 1.337090e+03 |
| SimpleExponentialSmoothing(α=0.3) | 1.900059e+03 |
| DoubleExponentialSmoothing(α=0.688) | 1.923793e+03 |
| NaiveModel | 3.979915e+03 |
| Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing | 1.381440e+04 |
| Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing | 1.381440e+04 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 |
| SARIMA(2,1,3)(1,0,3,12) | 5.093366e+27 |
Based on the model-building exercise, let's build the most optimum model(s) on the complete data and predict 12 months into the future with appropriate confidence intervals/bands
model = ExponentialSmoothing(df['Sparkling'],trend='additive',seasonal='multiplicative',freq='M')
# fit model
model_fit = model.fit(smoothing_level=0.3,smoothing_trend=0.3,smoothing_seasonal=0.3,optimized=False,use_brute=True)
# make prediction
yhat = model_fit.predict(start='31-08-1995',end='31-08-1996')
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:154: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing. date_key = Timestamp(key)
plt.figure(figsize=(18,9))
plt.plot(df['Sparkling'], label='Actual Dataset')
plt.plot(yhat,label='Alpha = Beta = Gamma = 0.3, Triple Exponential Smoothing Model')
plt.legend(loc='best')
plt.grid();
pred_1_df = pd.DataFrame({'lower_CI':yhat - 1.96*np.std(model_fit.resid,ddof=1),
'prediction':yhat,
'upper_ci':yhat + 1.96*np.std(model_fit.resid,ddof=1)})
pred_1_df.head()
| lower_CI | prediction | upper_ci | |
|---|---|---|---|
| 1995-08-31 | 1014.069331 | 1854.686308 | 2695.303286 |
| 1995-09-30 | 1650.369143 | 2490.986121 | 3331.603098 |
| 1995-10-31 | 2498.588142 | 3339.205119 | 4179.822096 |
| 1995-11-30 | 3415.513796 | 4256.130773 | 5096.747751 |
| 1995-12-31 | 6048.795192 | 6889.412170 | 7730.029147 |
pred_1_df.tail()
| lower_CI | prediction | upper_ci | |
|---|---|---|---|
| 1996-04-30 | 1573.333217 | 2413.950194 | 3254.567172 |
| 1996-05-31 | 1335.714857 | 2176.331834 | 3016.948811 |
| 1996-06-30 | 1212.044951 | 2052.661928 | 2893.278906 |
| 1996-07-31 | 1597.967939 | 2438.584916 | 3279.201894 |
| 1996-08-31 | 1522.187178 | 2362.804155 | 3203.421132 |
# plot the forecast along with the confidence band
axis = df.plot(label='Actual', figsize=(15,8))
pred_1_df['prediction'].plot(ax=axis, label='Forecast', alpha=0.5)
axis.fill_between(pred_1_df.index, pred_1_df['lower_CI'], pred_1_df['upper_ci'], color='k', alpha=.15)
axis.set_xlabel('Year-Months')
axis.set_ylabel('Sales')
plt.legend(loc='best')
plt.grid()
plt.show()
Trend Analysis: The forecasted values are increasing over time, suggesting a strong upward trend in the data.
Uncertainty in Forecasts:The confidence intervals (lower and upper CI) widen as we move further into the future, reflecting increased uncertainty in predictions over longer time horizons.
Seasonal Component:The predictions show a consistent seasonal influence, as the model adjusts based on recurring patterns in the data (e.g., periodic highs and lows).
The data shows clear seasonal patterns with spikes occurring at regular intervals, likely representing periodic increases in sales (e.g., due to holidays, seasonal demand, etc.).
There is also a noticeable trend in the data, with the sales level increasing over the years.
ABC Estate Wines should be prepared depending on this forecast to anticipate high and low sales periods and plan inventory, marketing, and staffing accordingly.
The forecast indicates continued growth in sales.which will support strategies to scale operations.
ABC Estate Wines should Ensure adequate stock during high-demand periods (e.g., late Q4 and early Q1 based on the peaks observed).
Plan promotions and production around these periods to maximize revenue without overstocking.
ABC Estate Wines should Promote other wine types during slower seasons to balance overall sales (e.g., offering discounts or special bundles).
The increasing trend in sales suggests growing demand. ABC Estate Wines can explore new markets or customer segments to sustain growth.